Over the past two decades, face recognition has been studied extensively. However, large intra-personal variations, such as pose, illumination and expression, remain challenging for robust face recognition in real-life photos.
Classical measurement approaches for face recognition have several limitations, which have restricted their wider applications in the scenarios of large intra-personal variations. For example, in the art these have been disclosed a method of the diverse distributions of face images for one person. In fact, these distributions generally have different densities, sizes, and shapes, due to the high complexity in face data. In addition, noise and outliers are often contained in face space. The current measurement approaches fail to tackle all of these challenges. Many methods based on (dis-)similarity measures directly use pair wise distances to compute the (dis-)similarities between faces, which cannot capture the structural information for the high-quality discrimination. Although some studies apply the structural information in measurements, the developed algorithms are, generally speaking, sensitive to noise and outliers. For instance, computing the length of the shortest path in a network is very sensitive to noisy nodes.